silver-layer-setup

Building a Silver layer pipeline with Spark Declarative Pipelines involves orchestrating end-to-end setup from scratch or Bronze-to-Silver transitions, integrating data quality rules, streaming ingestion, and monitoring views for your data lakehouse.

You add runtime-updateable data quality rules to a DLT Silver layer by integrating Delta table-based DQ rules and quarantine patterns, enabling robust data validation without restarting the pipeline.

To set up a Silver layer pipeline in Databricks, you need mandatory dependencies on common skills like databricks-table-properties and unity-catalog-constraints, plus domain skills such as dlt-expectations-patterns.

Yes, you can transition data from Bronze to Silver using Spark Declarative Pipelines by orchestrating Bronze-to-Silver transitions that incorporate streaming ingestion and automated data quality enforcement.

The best way to enforce data quality in a data lakehouse Silver layer is to use Spark Declarative Pipelines with integrated Delta table-based data quality rules and quarantine patterns for reliable data validation.